Building Machine Learning Systems with Python - Richert, Coelho

Computer Vision – Pattern Recognition
Both objects are against natural backgrounds and with large smooth areas inside the
objects. We therefore expect that textures will not be very good.
When we use the same features as before, we achieve 55 percent accuracy in
cross-validation using logistic regression. This is not too bad on four classes, but
not spectacular either. Let's see if we can use a different method to do better. In fact,
we will see that we need to combine texture features with other methods to get the
best possible results. But, first things first—we look at local features.
Local feature representations
A relatively recent development in the computer vision world has been the
development of local-feature-based methods. Local features are computed on a small
region of the image, unlike the previous features we considered, which had been
computed on the whole image. Mahotas supports computing a type of these features;
Speeded Up Robust Features, also known as SURF (there are several others, the most
well-known being the original proposal of Scale-Invariant Feature Transform (SIFT)).
These local features are designed to be robust against rotational or illumination
changes (that is, they only change their value slightly when illumination changes).
When using these features, we have to decide where to compute them. There are
three possibilities that are commonly used:
• Randomly
• In a grid
• Detecting interesting areas of the image (a technique known as keypoint
detection or interest point detection)
All of these are valid and will, under the right circumstances, give good results.
Mahotas supports all three. Using interest point detection works best if you have a
reason to expect that your interest point will correspond to areas of importance in the
image. This depends, naturally, on what your image collection consists of. Typically,
this is found to work better in man-made images rather than natural scenes. Manmade
scenes have stronger angles, edges, or regions of high contrast, which are the
typical regions marked as interesting by these automated detectors.
Since we are using photographs of mostly natural scenes, we are going to use the
interest point method. Computing them with mahotas is easy; import the right
submodule and call the surf.surf function:
from mahotas.features import surf
descriptors = surf.surf(image, descriptors_only=True)
[ 216 ]